mittermeieri-lehilahytsara exploration
2020-02-03
In the msp3 manuscript, we only included lehilahytsara samples from an area that is well north of the published IUCN range of the species, and some of which were originally labelled as mittermeieri.
So a question relevant to their (lack of) delimitation as species is what differentiation looks like when more southerly lehilahytsara populations are also included.
0.1 Maps
The map below shows the distributions of mittermeieri (green) and lehilahytsara (blue). IUCN distibutions have a darker color and a solid contour line; hand-drawn estimated “additional” distributions (based on our sampling sites and forest cover) have a lighter color and dotted contour lines.
The maps below shows the sites for which we have samples with RADseq data, colored by species (with remaining forest in the background). On the right, some sites that are very close to each other have been lumped together (e.g., “Anjiahely+” consists of “Anjiahely” and “Antsahabe”) – to avoid cluttering, I will use these lumped site names when visualizing PCA and ADMIXTURE results below.
Based on locations and genetic differentiation (see results below), I have divided the lehilahytsara samples into three “population groups”: “lehi-north”, “lehi-south”, and “lehi-high” (highland plateau sites, which is only Ankafobe after filtering). See the sites maps below, now colored by population group. For the msp3 paper, we only included lehilahytsara samples from the “lehi-north” population group (Riamalandy and the Ambavala area).
Finally, here is a map simply showing sampling sites by population group, with vegetation types of remaining vegetation shown:
0.2 RADseq metadata
Samples were genotyped with Stacks and then filtered using a fairly stringent filtering procedure which removes both sites and samples. In the tables below, the “pass” indicates samples that passed this filtering, and “fail” indicates samples that failed this filtering step. (Not all samples that “failed” this are useless though, given the stringency of the filtering.) I did not include the two or so samples from captive colonies.
- Number of samples by population group (“pop_group”):
pop_group pass fail total mittermeieri 8 5 13 lehi: north 8 2 10 lehi: high 7 7 14 lehi: south 11 6 17 - Number of samples by site:
site_short pass fail total pop_group Ambatovy 4 0 4 lehi: south Ambavala 5 1 6 lehi: north Amboasary 1 1 2 lehi: south Ambohitanely 0 4 4 lehi: high Anjanaharibe 2 3 5 mittermeieri Anjiahely 3 1 4 mittermeieri Ankafobe 7 3 10 lehi: high Anosibe 1 0 1 lehi: south Antsahabe 1 0 1 mittermeieri Madera 2 0 2 lehi: north Mantadia 1 2 3 lehi: south Marojejy 2 1 3 mittermeieri Riamalandy 1 1 2 lehi: north Sahanody 0 1 1 lehi: south Tsinjoarivo 4 2 6 lehi: south - List of all samples:
ID Sample_ID sp site_short site_lump pop_group filtering mmit001 MBB012 mmit Anjanaharibe Anjanaharibe mittermeieri fail mmit002 MBB013 mmit Anjanaharibe Anjanaharibe mittermeieri fail mmit003 MBB014 mmit Anjanaharibe Anjanaharibe mittermeieri fail mmit004 MBB016 mmit Anjanaharibe Anjanaharibe mittermeieri pass mmit019 PBZT115 mmit Anjanaharibe Anjanaharibe mittermeieri pass mmit008 04-07_hely mmit Anjiahely Anjiahely+ mittermeieri fail mmit009 07-06_habe mmit Antsahabe Anjiahely+ mittermeieri pass mmit010 08-07_hely mmit Anjiahely Anjiahely+ mittermeieri pass mmit011 10-07_hely mmit Anjiahely Anjiahely+ mittermeieri pass mmit012 2-07_hely mmit Anjiahely Anjiahely+ mittermeieri pass mmit005 MBB005 mmit Marojejy Marojejy mittermeieri fail mmit006 RMR186 mmit Marojejy Marojejy mittermeieri pass mmit007 RMR187 mmit Marojejy Marojejy mittermeieri pass mmit013 B12 mleh Ambavala Ambavala+ lehi: north pass mmit014 B14 mleh Ambavala Ambavala+ lehi: north pass mmit015 BC3 mleh Madera Ambavala+ lehi: north pass mmit016 C12 mleh Ambavala Ambavala+ lehi: north pass mmit017 C23 mleh Ambavala Ambavala+ lehi: north pass mmit018 C24 mleh Madera Ambavala+ lehi: north pass mspp016 B23 mleh Ambavala Ambavala+ lehi: north fail mspp019 BC2 mleh Ambavala Ambavala+ lehi: north pass mleh019 JMR001 mleh Riamalandy Riamalandy lehi: north fail mleh020 JMR002 mleh Riamalandy Riamalandy lehi: north pass mleh005 RMR95 mleh Ambohitanely Ambohitanely lehi: high fail mleh006 RMR96 mleh Ambohitanely Ambohitanely lehi: high fail mleh007 RMR97 mleh Ambohitanely Ambohitanely lehi: high fail mleh008 RMR99 mleh Ambohitanely Ambohitanely lehi: high fail mleh009 MBB036 mleh Ankafobe Ankafobe lehi: high fail mleh010 MBB037 mleh Ankafobe Ankafobe lehi: high pass mleh011 MBB038 mleh Ankafobe Ankafobe lehi: high pass mleh012 MBB039 mleh Ankafobe Ankafobe lehi: high fail mleh013 MBB040 mleh Ankafobe Ankafobe lehi: high pass mleh014 MBB041 mleh Ankafobe Ankafobe lehi: high pass mleh015 MBB042 mleh Ankafobe Ankafobe lehi: high pass mleh016 MBB043 mleh Ankafobe Ankafobe lehi: high pass mleh017 MBB044 mleh Ankafobe Ankafobe lehi: high fail mleh018 MBB045 mleh Ankafobe Ankafobe lehi: high pass mleh001 JMR092 mleh Ambatovy Ambatovy+ lehi: south pass mleh002 MBB001 mleh Ambatovy Ambatovy+ lehi: south pass mleh003 MBB002 mleh Ambatovy Ambatovy+ lehi: south pass mleh004 MBB003 mleh Ambatovy Ambatovy+ lehi: south pass mleh027 01-00_Man mleh Mantadia Ambatovy+ lehi: south fail mleh028 02-00_Man mleh Mantadia Ambatovy+ lehi: south fail mleh031 ODY6.9 mleh Sahanody Ambatovy+ lehi: south fail mleh033 TAD4.32 mleh Mantadia Ambatovy+ lehi: south pass mleh029 ANJZ11 mleh Amboasary Amboasary lehi: south pass mleh030 ANJZ20 mleh Amboasary Amboasary lehi: south fail mleh032 SIB7.1 mleh Anosibe Anosibe lehi: south pass mleh021 DWW3235 mleh Tsinjoarivo Tsinjoarivo lehi: south pass mleh022 DWW3236 mleh Tsinjoarivo Tsinjoarivo lehi: south fail mleh023 DWW3243 mleh Tsinjoarivo Tsinjoarivo lehi: south pass mleh024 DWW3244 mleh Tsinjoarivo Tsinjoarivo lehi: south pass mleh025 DWW3249 mleh Tsinjoarivo Tsinjoarivo lehi: south pass mleh026 DWW3250 mleh Tsinjoarivo Tsinjoarivo lehi: south fail
0.3 Jordi’s tree
Here is part of Jordi’s tree that he sent around a while ago. This seems to suggest that the main axis of differentiation is between mittermeieri + “lehi-north” on one hand versus “lehi-south” on the other (note that no samples from “lehi-high” were included). At its most extreme, this could mean that one could in fact distinguish two species, but that “lehi-north” would just have to be included with mittermeieri…
0.4 Splitstree
Below is an annotated Splitstree network, with sites and population groups labelled. This is roughly in line with Jordi’s tree, but it’s fairly messy, and might rather suggest an isolation-by-distance (IBD) pattern that would show even less support for distinct groups if geographically intermediate populations were included.
0.5 ADMIXTURE
Below is an ADMIXTURE plot showing cross-validation errors at different values of K. This suggests that the fit gets worse with a higher number of clusters, already slightly so going from K=1 -> K=2. So no support for two species.
Below is an ADMIXTURE plot with K=2, K=3, and K=4. At K=2, “lehi-north” is grouped with mittermeieri, like in Jordi’s tree. At K=3, “lehi-high” gets its own cluster. At K=4, each of the population groups gets its own cluster.
0.6 PCA
Below is a PCA plot, with ellipses around each (lumped) site, and a different point shape for each population group. The inset just shows colors by population group. I’d say these results are also consistent with a rough IBD pattern going from “lehi-south” -> “lehi-north”" -> mittermeieri, but with the highland population group relatively distinct.
0.7 IBD
Genetic versus geographic distances by population, with “interspecific” ( lehilahytsara versus mittermeieri) comparisons in red and intraspecific comparisons in blue. The highest genetic differentiation is interspecific but also at the largest geographic distances. There is no big jump like we saw for msp3 versus macarthuri.
Interactive version of the IBD plot:
0.8 Concluding
- I’d say this doesn’t support two (or more) species, so in that sense we’re on solid ground if we want to recommend to lump mittermeieri and lehilahytsara in the msp3 manuscript.